Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training
arXiv:1804.02586
Abstract
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain. In comparison, massive unlabeled 3D CT volumes are usually easily accessible. Current mainstream works to address the semi-supervised biomedical image segmentation problem are mostly graph-based. By contrast, deep network based semi-supervised learning methods have not drawn much attention in this field. In this work, we propose Deep Multi-Planar Co-Training (DMPCT), whose contributions can be divided into two folds: 1) The deep model is learned in a co-training style which can mine consensus information from multiple planes like the sagittal, coronal, and axial planes; 2) Multi-planar fusion is applied to generate more reliable pseudo-labels, which alleviates the errors occurring in the pseudo-labels and thus can help to train better segmentation networks. Experiments are done on our newly collected large dataset with 100 unlabeled cases as well as 210 labeled cases where 16 anatomical structures are manually annotated by four radiologists and confirmed by a senior expert. The results suggest that DMPCT significantly outperforms the fully supervised method by more than 4% especially when only a small set of annotations is used.
arXiv commentaccepted by WACV2019
References in corpus (4)
- A Survey on Multi-view Learning
- Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
- Hierarchical 3D fully convolutional networks for multi-organ segmentation
- Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
Cited by in corpus (7)
- Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
- Constrained-CNN losses for weakly supervised segmentation
- Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
- Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
- Revisiting CycleGAN for semi-supervised segmentation
- Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels
- Diversified Multi-prototype Representation for Semi-supervised Segmentation